It is common to analyze large data sets in the process of making business decisions. Such data sets may be thought of as comprising a dimensionally-modeled fact collection. For example, each “record” of the fact collection may represent attributes of an “item” or “entity” such as a particular user of online services, whereas the value at each field of the record represents a value of a particular characteristic of that entity (e.g., age of user, gender of user, number of online page views by that user, etc.). It is known to provide a visual representation of the dimensionally-modeled fact collections as an analysis tool for use in the process of making business decisions.
When interacting with and/or analyzing large data sets, each data set may have many record—millions or more. It can be difficult or impractical to consider all the records individually. Thus, for example, users may prefer to aggregate records together based on values of a particular one or more of the characteristics of the item represented by the record.
It is desirable to provide tools that facilitate the definition of such aggregation.